This work provides a comprehensive framework for noise-aware quantum program analysis, ranging from logical foundations to model checking and automated synthesis. While most research on quantum programming considers an idealized, noise-free semantics for quantum programs, we reason about quantum programs that are executed on real, noisy hardware. We use the error models published by quantum-hardware vendors to give a hardware-dependent semantics to quantum programs. We then present a noise-aware quantum Hoare logic with a relatively complete proof system. We finally use state-space exploration techniques for the hardware-aware verification and synthesis of loop-free quantum programs. In this way, we synthesize optimal, hardware-dependent solutions for parity bit-flip, GHZ state preparation, and other problems that commonly occur as subroutines in larger quantum algorithms. We evaluate our method on all 55 hardware specifications provided by the IBM Qiskit toolkit. Finally, we also show that classical probabilistic branching is needed for optimality in quantum programming.
This work provides a comprehensive framework for noise-aware quantum program analysis, ranging from logical foundations to model checking and automated synthesis. While most research on quantum programming considers an idealized, noise-free semantics for quantum programs, we reason about quantum programs that are executed on real, noisy hardware. We use the error models published by quantum-hardware vendors to give a hardware-dependent semantics to quantum programs. We then present a noise-aware quantum Hoare logic with a relatively complete proof system. We finally use state-space exploration techniques for the hardware-aware verification and synthesis of loop-free quantum programs. In this way, we synthesize optimal, hardware-dependent solutions for parity bit-flip, GHZ state preparation, and other problems that commonly occur as subroutines in larger quantum algorithms. We evaluate our method on all 55 hardware specifications provided by the IBM Qiskit toolkit. Finally, we also show that classical probabilistic branching is needed for optimality in quantum programming.
Quantum hardware is inherently fragile and noisy. We find that the accuracy of traditional quantum error correction algorithms can be improved depending on the hardware. Given different hardware specifications, we automatically synthesize hardware-optimal algorithms for parity correction, qubit resetting, and GHZ (Greenberger-Horne-Zeilinger) state preparation. Using stochastic techniques from computer science, our method presents a computational tool to compute exact accuracy guarantees and synthesize optimal algorithms that are often different from traditional ones. We also show that improvements can be gained with respect to the Qiskit transpiler as we compute the hardware-optimal qubit mapping for the GHZ state-preparation problem.
The rapid advancement of Noisy Intermediate-Scale Quantum (NISQ) devices has driven the need for efficient design automation tools that can translate complex engineering models into executable quantum circuits. This study explores the development of automated quantum circuit design frameworks focused on gate optimization and noise-aware compilation, enabling practical quantum computation for engineering applications such as optimization, simulation, and control systems. Traditional circuit design methods often fail to account for hardware constraints, decoherence, and gate infidelity, which significantly affect computational accuracy on NISQ devices. To address these challenges, this research introduces a hybrid automation approach combining quantum transpilation, variational circuit pruning, and error-aware synthesis algorithms to enhance circuit reliability and scalability. The framework integrates quantum-classical co-design principles to optimize both logical and physical circuit layers, leveraging hardware-specific calibration data to adapt compilation strategies dynamically. Through simulations and benchmark analysis, the proposed system demonstrates significant improvements in quantum resource utilization, error mitigation, and gate depth reduction compared to conventional compilation methods. This work underscores the growing importance of design automation in quantum engineering, where algorithmic efficiency must be harmonized with physical constraints to realize functional and scalable quantum workflows. By establishing a methodology for gate-level optimization and noise-adaptive design, the study contributes to bridging the gap between theoretical quantum algorithms and real-world engineering implementations within the NISQ computing era.
Abstract Suppressing errors is one of the central challenges in achieving reliable quantum computation on near-term hardware. While much attention has been paid to error correction and mitigation, we identify quantum circuit structure itself as a powerful lever for proactive error suppression. In this work, we present a unified and hardware-adaptive framework for noise-aware quantum circuit design, in which circuit topology and parameters are co-optimized from the ground up based on the noise profile of the target backend. Our framework supports a wide range of quantum tasks-including circuit compilation, quantum state preparation, and unitary approximation-under a consistent optimization paradigm. Extensive experiments on five IBM backends confirm the effectiveness of our method, showing significant fidelity gains across all tasks under realistic noise. These results demonstrate that noise suppression through structure-aware design offers a powerful and generalizable strategy for enhancing circuit performance on NISQ hardware. Our framework bridges low-level hardware constraints with high-level circuit synthesis, paving the way for more robust and efficient quantum programming in the presence of noise.
Noise and imperfections are among the prevalent challenges in quantum software engineering for current NISQ systems. They will remain important in the post-NISQ area, as logical, error-corrected qubits will be based on software mechanisms. As real quantum hardware is still limited in size and accessibility, noise models for classical simulation-that in some cases can exceed dimensions of actual systems-play a critical role in obtaining insights into quantum algorithm performance, and the properties of mechanisms for error correction and mitigation. We present, implement and validate a tunable noise model building on the Kraus channel formalism on a large scale quantum simulator system (Qaptiva). We use empirical noise measurements from IBM quantum (IBMQ) systems to calibrate the model and create a realistic simulation environment. Experimental evaluation of our approach with Greenberger-Horne-Zeilinger (GHZ) state preparation and QAOA applied to an industrial usecase validate our approach, and demonstrate accurate simulation of hardware behaviour at reasonable computational cost. We devise and utilise a method that allows for determining the quality of noise models for larger problem instances than is possible with existing metrics in the literature. To identify potentials of future quantum software and algorithms, we extrapolate the noise model to future partially fault-tolerant systems, and give insights into the interplay between hardware-specific noise modelling and hardware-aware algorithm development.
Quantum programming is hard: Quantum programs are necessarily probabilistic and impossible to examine without disrupting the execution of a program. In response to this challenge, we and a number of other researchers have written tools to verify quantum programs against their intended semantics. This is not enough. Verifying an idealized semantics against a real world quantum program doesn't allow you to confidently predict the program’s output. In order to have verification that works, you need both an error semantics related to the hardware at hand (this is necessarily low level) and certified compilation to the that same hardware. Once we have these two things, we can talk about an approach to quantum programming where we start by writing and verifying programs at a high level, attempt to verify properties of the compiled code, and repeat as necessary.
Noisy Intermediate-Scale Quantum (NISQ) hardware has transformed quantum computing into a practical experimental platform while introducing fundamental challenges associated with hardware noise, decoherence, and probabilistic execution. Although quantum software testing (QST) has rapidly emerged as an active research area, it remains unclear to what extent existing testing methodologies explicitly engage with noisy and hardware-constrained execution environments. This paper presents a systematic empirical landscape analysis of noise-awareness in QST research through a deterministic, taxonomy-guided analytical framework applied to 61 papers published between 2018 and 2025 across eight testing paradigms, contrasting how contributions are framed with how they are evaluated. Among the analyzed papers, 47 (77.0%) are noise-oblivious and only 14 (23.0%) explicitly engage with noise-related execution concerns, while only 11 (18.0%) report evaluation on physical hardware or a noisy simulator. Test generation, the largest paradigm, exhibits a noise-awareness rate of only 14.3%, and debugging and repair 18.2%; coverage-based, metamorphic, and differential testing contain no noise-aware contributions. Noise-aware methodologies emerged late and remain outnumbered in all analyzed years. Framing and evaluation are strongly associated (odds ratio 40.5, p < 0.001) yet diverge for individual papers: 5 of the 14 noise-aware papers evaluate only on ideal simulators. Inferential statistical tests are reported in only 16.4% of papers. The classification framework was validated against a blinded, human-coded reference sample, achieving almost perfect inter-rater agreement on testing-technique classification and substantial agreement on noise-awareness. The framework provides a reusable empirical basis for longitudinal analysis of hardware-conscious testing practice.